Two modes of degradation of the tramtrack transcription factors by Siah homologues

MIGGRI: A multi-instance graph neural network model for inferring gene regulatory networks for Drosophila from spatial expression images

Recent breakthrough in spatial transcriptomics has brought great opportunities for exploring gene regulatory networks (GRNs) from a brand-new perspective. Especially, the local expression patterns and spatio-temporal regulation mechanisms captured by spatial expression images allow more delicate delineation of the interplay between transcript factors and their target genes. However, the complexity and size of spatial image collections pose significant challenges to GRN inference using image-based methods. Extracting regulatory information from expression images is difficult due to the lack of supervision and the multi-instance nature of the problem, where a gene often corresponds to multiple images captured from different views. While graph models, particularly graph neural networks, have emerged as a promising method for leveraging underlying structure information from known GRNs, incorporating expression images into graphs is not straightforward. To address these challenges, we propose a two-stage approach, MIGGRI, for capturing comprehensive regulatory patterns from image collections for each gene and known interactions. Our approach involves a multi-instance graph neural network (GNN) model for GRN inference, which first extracts gene regulatory features from spatial expression images via contrastive learning, and then feeds them to a multi-instance GNN for semi-supervised learning. We apply our approach to a large set of Drosophila embryonic spatial gene expression images. MIGGRI achieves outstanding performance in the inference of GRNs for early eye development and mesoderm development of Drosophila, and shows robustness in the scenarios of missing image information. Additionally, we perform interpretable analysis on image reconstruction and functional subgraphs that may reveal potential pathways or coordinate regulations. By leveraging the power of graph neural networks and the information contained in spatial expression images, our approach has the potential to advance our understanding of gene regulation in complex biological systems.

Coordinated repression and activation of two transcriptional programs stabilizes cell fate during myogenesis

Molecular models of cell fate specification typically focus on the activation of specific lineage programs. However, the concurrent repression of unwanted transcriptional networks is also essential to stabilize certain cellular identities, as shown in a number of diverse systems and phyla. Here, we demonstrate that this dual requirement also holds true in the context of Drosophila myogenesis. By integrating genetics and genomics, we identified a new role for the pleiotropic transcriptional repressor Tramtrack69 in myoblast specification. Drosophila muscles are formed through the fusion of two discrete cell types: founder cells (FCs) and fusion-competent myoblasts (FCMs). When tramtrack69 is removed, FCMs appear to adopt an alternative muscle FC-like fate. Conversely, ectopic expression of this repressor phenocopies muscle defects seen in loss-of-function lame duck mutants, a transcription factor specific to FCMs. This occurs through Tramtrack69-mediated repression in FCMs, whereas Lame duck activates a largely distinct transcriptional program in the same cells. Lineage-specific factors are therefore not sufficient to maintain FCM identity. Instead, their identity appears more plastic, requiring the combination of instructive repressive and activating programs to stabilize cell fate.

Lotus japonicus E3 ligase SEVEN IN ABSENTIA4 destabilizes the symbiosis receptor-like kinase SYMRK and negatively regulates rhizobial infection

The Lotus japonicus SYMBIOSIS RECEPTOR-LIKE KINASE (SYMRK) is required for symbiotic signal transduction upon stimulation of root cells by microbial signaling molecules. Here, we identified members of the SEVEN IN ABSENTIA (SINA) E3 ubiquitin-ligase family as SYMRK interactors and confirmed their predicted ubiquitin-ligase activity. In Nicotiana benthamiana leaves, SYMRK-yellow fluorescent protein was localized at the plasma membrane, and interaction with SINAs, as determined by bimolecular fluorescence complementation, was observed in small punctae at the cytosolic interface of the plasma membrane. Moreover, fluorescence-tagged SINA4 partially colocalized with SYMRK and caused SYMRK relocalization as well as disappearance of SYMRK from the plasma membrane. Neither the localization nor the abundance of Nod-factor receptor1 was altered by the presence of SINA4. SINA4 was transcriptionally upregulated during root symbiosis, and rhizobia inoculated roots ectopically expressing SINA4 showed reduced SYMRK protein levels. In accordance with a negative regulatory role in symbiosis, infection thread development was impaired upon ectopic expression of SINA4. Our results implicate SINA4 E3 ubiquitin ligase in the turnover of SYMRK and provide a conceptual mechanism for its symbiosis-appropriate spatio-temporal containment.

The Siah2-HIF-FoxA2 axis in prostate cancer – new markers and therapeutic opportunities

Recent studies indicate the importance of the ubiquitin ligase Siah2 in control of more aggressive prostate tumors – namely, neuroendocrine (NE) prostate tumors and prostate adenocarcinoma (PCa) harboring neuroendocrine lesions. Siah2-dependent expression and activity of HIF-1α regulate its availability to form a transcriptional complex with FoxA2, resulting in expression of specific target genes, including Hes6, Sox9 and Jmjd1a, whose co-expression is sufficient for formation of NE tumors and NE lesions in PCa. These studies provide novel markers to diagnose and monitor formation of NE lesions and NE tumors. Furthermore, defining the regulatory axis consisting of Siah2 and HIF-1α/FoxA2 cooperation suggests novel therapeutic modalities to treat these most aggressive forms of prostate cancer. Here we review current understanding of Siah role in control of hypoxia and prostate tumor development and highlight potential approaches for targeting components along Siah-regulated pathways.

The ubiquitin ligase Siah2 regulates tumorigenesis and metastasis by HIF-dependent and -independent pathways

The ubiquitin ligase Siah2 has been shown to regulate prolyl hydroxylase 3 (PHD3) stability with concomitant effect on HIF-1alpha availability. Because HIF-1alpha is implicated in tumorigenesis and metastasis, we used SW1 mouse melanoma cells, which develop primary tumors with a propensity to metastasize, in a syngeneic mouse model to assess a possible role for Siah2 in these processes. Inhibiting Siah2 activity by expressing a peptide designed to outcompete association of Siah2-interacting proteins reduced metastasis through HIF-1alpha without affecting tumorigenesis. Conversely, inhibiting Siah2 activity by means of a dominant-negative Siah2 RING mutant primarily reduced tumorigenesis through the action of Sprouty 2, a negative regulator of Ras signaling. Consistent with our findings, reduced expression of PHD3 and Sprouty2 was observed in more advanced stages of melanoma tumors. Using complementary approaches, our data establish the role of Siah2 in tumorigenesis and metastasis by HIF-dependent and -independent mechanisms.